Machine Learning Team Lead
Position Summary:Our partner, one of the U.S.'s most respected healthcare systems, is rethinking how practical machine learning and AI blend within a development-driven environment. As the lead technical contact, you’ll help teams apply both AI tooling and ML models to build smarter applications and workflows. You’ll provide guidance on data usage, model integration, LLM adoption, and intelligent automation across multiple development groups. The systems you support are data-heavy and influence real operational and clinical decisions. You’ll prototype, experiment, and introduce practical solutions that improve productivity and insight. This is a hands-on role with broad visibility and real user impact, ideal for someone who enjoys working at the intersection of AI, machine learning, and engineering.Experience and Education:BS in Computer Science, Data Science, Information Technology, or related fieldBackground in Software Development, AI engineering, or machine learning within complex product environmentsExperience supporting multiple engineering teams or working within large-scale software organizationsHands-on work with both AI tooling (LLMs, copilots) and traditional ML development practicesExposure to data-rich systems that support operational or analytical decision-makingFamiliarity with cloud-native development environmentsSkills and Strengths:PythonMachine LearningLLMs integrationAI ToolingJavaScriptReact / Next.jsNode.js / NestJSData Analysis & Modeling ConceptsModel EvaluationAlgorithmic ThinkingAPIsSQLCloud ArchitectureTesting AutomationSystem DesignVersion ControlCI/CD PipelinePrimary Job Responsibilities:Guide teams on using AI tooling and ML practices to accelerate development, testing, and researchIntegrate AI-driven and ML-driven features into existing and new applicationsBuild proofs-of-concept showcasing how AI/ML can improve productivity, insight, and decision-makingCollaborate with product, data, and engineering leads to shape long-term AI/ML strategy and roadmapsTranslate complex AI and ML concepts into practical engineering guidance and workflowsEnsure AI models, ML pipelines, and automated workflows remain reliable, safe, and scalableContribute to core software and application development when neededReview architecture and provide direction on AI/ML-enabled patterns and best practicesDrive technical decision-making around model usage, integration, deployment, and performancePromote responsible and ethical approaches to AI and ML adoption within engineering teamsSupport the rollout and evaluation of LLM platforms like Claude and other emerging AI tools